Twitter

The Enterprise 2.0 conference is about to get underway in Boston. The event focuses on all the ways social media tools that are familiar on the consumer Internet are making their way behind the firewall in many enterprises and institutions. Why can’t you “friend” a colleague or “like” a spreadsheet or slide deck? Employees often come to their jobs expecting tools that resemble the social media tools with which they already spend much of their time.

Like many conferences, this one has a hashtag, actually two that I know of: #e2 and #e2conf. There is already a good deal of activity leading up to the event. Here is a map of connections among a group of people who mentioned either #e2 or #e2conf in the last few days.

In this map there are 532 Vertices and 9,395 Unique Edges, creating 13 Connected Components, 11 of which had only a Single-Vertex, the largest component had 519 vertices which were interconnected 9,393 times. The small number of isolated components indicates that this is a cohesive community of highly connected participants. These people know and follow, reply and mention one another. The Graph had a Density of 0.03 and the Maximum Geodesic Distance (Diameter) was 5 steps with an Average Geodesic Distance of 2.

Within this mass of connected users is a core group of highly “between” people, those who most broadly span connections within the population. These are one possible set of “influentials” within the Enterprise 2.0 community.

Here is a two screen view of the list of the top most between #e2 OR #e2conf mentioning twitter users along with the overview graph of their internal linkages.

A closer look at the graph alone can reveal enough detail to read the names of these central participants.

This is a view of the list of authors sorted in Excel by their “Betweenness centrality” score, the measure of how much these people “bridge” across the network.

An alternative view plots these contributors in an X/Y space based on their count of followers (along the x axis) and count of tweets (along the y axis).

Twitter users who mentioned #e2 or #e2conf on June 13, 2010 scaled by number of followers, x = log(followers), y = log(tweets).

There is a correlation between tweets and followers, but not everyone converts tweets to followers at the same rate. Below the diagonal are those who over convert tweets to followers, those above the diagonal under convert tweets to followers.

Hello! Social media network maps reveal the key people, groups, and topics discussed in a public conversation.
If you would like to request a custom social media network map made with NodeXL for the topic, hashtag, URL, or username of your choice complete the form below. I will generate the maps as requests come in and email you a pointer to the results which I will post to the NodeXL Graph Gallery: See – https://nodexlgraphgallery.org/Pages/Default.aspx

The visualization represents the connections among 1699 Twitter users over a 2-day, 21-hour, 48-minute period from Wednesday, 08 January 2014 at 02:53 UTC to Saturday, 11 January 2014 at 00:42 UTC.

In the sample map above for the term “CustServ” the visualization represents the connections among 1699 Twitter users over a 2-day, 21-hour, 48-minute period from Wednesday, 08 January 2014 at 02:53 UTC to Saturday, 11 January 2014 at 00:42 UTC.

The most central and possibly “influential” contributors to this discussion are:

Deliberation: Honeycutt and Herring – Twitter not only used for one-way comm, but 31% of all tweets direct a specific addressee. Kroop and Jansen – political internet discussion boards dominated by small # of heavy users

Sentiment: How accurately can Twitter inform us about the electorate’s political sentiment?

Prediction: can Twitter serve as a predictor of the election result?

Data: examined more than 100k tweets and extracted their sentiment using LIWC

Target: German federal election 2009

Results:

1. While Twitter is used as a forum for political deliberation on substantive issues, this forum is dominated by heavy users

-Equality of participaion: While the distribution of users across groups is almost identical with the one found on internet message boards, we find even less equality of participation for the political debate on Twitter. Additional analyses have shown users to exhibit a party-bias in the volume and sentiment of messages.

2. The online sentiment in tweets reflects nuanced offline differences between the politicians in our sample.

LIWC profiles:

-Leading candidates: Very similar profile for all leading candidates, only polarizing political characters, such as liberal leader and socialist, deviate in line with their roles as opposition leaders. Messages mentioning Steinmeir (coalition leader) are most tentative

3. Similarity of profiles is a plausible reflection of the political proximity between the parties

Key findings: high convergence of leading candidates, more divergence among politicians of governin grand coalition than among those of a potential right wing coalition

4. Activity on Twitter prior to election seems to validly reflect the election outcome (MAE 1.65%), and joint party mentions accurately reflect the political ties between parties.

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (Brendan O’Connor)

Can useful observations be made by studying the social media sea one bucket at a time?

NodeXL has data import “spigots” for pulling social networks out of several social media systems including Twitter, YouTube, flickr, and email. Twitter networks of follows and followers, reply and mentions can be extracted based on either a user name or a search string “seed”. There are additional networks inside Twitter: a tie is created whenever two people tweet the same URL, for example, or are connected by tweeting from the same general location. For now, the NodeXL Twitter Data Importer is starting with these three initial twitter “tie-types”.

NodeXL queries are not exhaustive collections of Twitter data, we provide a more modest approach, grabbing a slice of recent content and analyzing that. Twitter has a sea of data, NodeXL is importing something like a study of buckets of ocean water. A recent scientific voyage to the Great Pacific Garbage Gyre, for example, collected hundreds of samples of ocean water as they sailed to the central location of the gyre. Each bucket revealed details about the larger state of the ocean (which does not look good). Simlarly, NodeXL is puling buckets of social media network data from the ocean of twitter and, despite the lack of scale, can do some useful science. In part this is a virtue imposed by necessity – constraints imposed by Twitter (even with a rate limit lifted “whitelisted” account) impose significant limits on what can be squeezed out of the Twitter API. For those who lack access to large data center resources, there are scale limits imposed by the capacities of a desktop/laptop device.

Access to large data sets is certainly a hallmark of the “new era of science” that generates observations not from samples but from exhaustive surveys of data terrains. Small samples miss important phenomena it is argued. The counter argument is that many important phenomena appear in most samples, even small ones.

Using the existing features in NodeXL, I can extract the twitter social network for a small group of user accounts. I can provide the names or ask twitter search to deliver them. Alternatively, a keyword can be used to collect all the users and their connections who recently tweeted containing that term. From this selected sample, several observations can be made:

> Not every keyword is equally connected

> Not every twitter user is equally connected nor are their neighbors

> Selected data extractions can be useful in the absence of a global view

The oil spill disaster in the Gulf of Mexico is a topic of great concern to many people in twitter.

This is the map of connections among people who tweeted the term “oil spill”. There are lots of isolated authors, people who tweeted “oil spill” but are not connected to anyone else who said the same phrase. The “giant component” is relatively sparse, there is no dense core of “oil spill” people yet. This is in contrast with many topics where a large, highly interconnected cluster of people defines the “center” of the discussion. This term remains highly diffuse.

This is the map of connections among people who tweeted the term “BP”.

The BP map also has a large population of isolates, people who are not part of a “BP” related conversation but have said the term. A small core of highly interconnected users is forming in the “BP” hashtag space but in contrast to a term like “solar”, there is still little cohesion and density in its core.

This is the map of connections among people who tweeted the term “solar”.

This is a topic with a more dense core of highly interconnected Twitter users who share the use of the string “solar” in their tweets. Ranks of isolates are also present in this topic space but the giant component is more internall connected and cohesive.

The eruption of Eyjafjallajokull in Iceland has created disruption of air traffic around the world and particularly through Northern Europe. A similar eruption of tweets has followed, focused around a series of hastags including “#ashtag”, “#ashcloud”, and “#volcano”.

The #ashtag network is highlighted by well known technology bloggers.

In contrast the map for the tag “#ashcloud” is dominated by the UK and Scottish travel and Foreign office twitter accounts.

This image features a minor but useful tip for building multi-line tooltips. This can help pack a lot of information about a Node into the tooltip. To build a multi-line tooltip use the “&CHAR(10)&” feature to glue other columns of data together along with labels.

Add a column to the Vertices worksheet and paste something like this into it:

Which creates a tooltip with multiple lines containing the Twitter User name, the number of their followers, a new line containing their user description followed by their last tweet on another line. Any concatenation of columns contents can be assembled in this way.

We have several data import providers (spigots) in NodeXL that query popular sources of social media for information that can be processed into a network graph. User and search term networks from Twitter, YouTube, and flickr have been implemented for a while along with a connector to email reply networks through the Windows Search Index. NodeXL also imports data from several popular network analysis file formats, opening up data sets and sample libraries used in many courses.